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This study introduces a residual deep reinforcement learning framework that enhances computed torque control for cable-driven lower-limb rehabilitation robots, addressing challenges posed by model uncertainty and external disturbances. By integrating a bounded Deep Deterministic Policy Gradient policy to provide compensating torque, the approach maintains the interpretability of conventional controllers while significantly improving trajectory tracking and disturbance rejection. The evaluation shows that this method not only meets various operational constraints but also demonstrates robustness across diverse simulated conditions, suggesting its potential for real-world applications in rehabilitation robotics.
Residual learning can enhance the robustness of rehabilitation robots without sacrificing the interpretability of traditional control methods.
Accurate trajectory tracking in cable-driven lower-limb rehabilitation robots is challenging because model uncertainty, external disturbances, joint constraints, and pull-only cable actuation can degrade nominal control performance. Conventional model-based controllers provide an interpretable control structure but remain sensitive to model mismatch, whereas fully learning-based control can reduce transparency and complicate constraint-aware operation. This study proposes a residual deep reinforcement learning-enhanced computed torque control framework in which computed torque control generates the nominal command and a bounded Deep Deterministic Policy Gradient policy supplies only an additional compensating torque. The approach is evaluated in simulation under nominal, uncertain, disturbed, combined, and generalization conditions, together with trajectory-tracking, joint-limit, cable-demand, workspace-feasibility, and cable-Jacobian diagnostics. Across the evaluated conditions, the residual controller improves tracking and disturbance rejection relative to computed torque control while preserving the interpretable model-based command structure and satisfying the reported feasibility checks in the representative evaluation. Broader tests indicate that tracking improvements can persist beyond the representative case while also exposing trajectory-dependent constraint limitations. These results support bounded residual learning as a practical robustness-enhancement strategy for simulation-based rehabilitation robot control and motivate further constraint-aware and experimental validation.
